A Wearable Indoor Navigation System with Context
Based Decision Making For Visually Impaired
Xiaochen Zhang1*, Jizhong Xiao1, Bing Li1, Pablo Munoz2,
Samleo L Joseph1, YiSun1, Chucai Yi2 and Yingli Tian1
1Department of Electrical Engineering, the City College of City University of New York, New York, NY 10031, USA
2Department of Computer Science, the Graduate Center of City University of New York, New York, NY 10016, USA
Xiaochen Zhang, Department of Electrical Engineering, the City College of City University of New York, New York, NY10031, USA,Tel: +919861932338; E-mail:
Received: October 31, 2016; Accepted: November 05, 2016; Published: November 15, 2016
Zhang X, Xiao J, Li B, Muñoz P, Joseph SL, et al. (2016) A Wearable Indoor Navigation System with Context Based Decision Making For Visually Impaired. Int J Adv Robot Automn 1(3): 1-11. DOI: 10.15226/2473-3032/1/3/00115
This paper presents a wearable indoor navigation system
that helps visually impaired user to perform indoor navigation.
The system takes advantage of the Simultaneous Localization
and Mapping (SLAM) and semantic path planning to accomplish
localization and navigation tasks while collaborating with the visually
impaired user. It integrates multiple sensors and feedback devices as
an RGB-D camera, an IMU and a web camera; and it applies the RGB-D
based visual odometry algorithm to estimate the user’s location
and orientation, and uses the IMU to refine the orientation error.
Major landmarks such as room numbers and corridor corners are
detected by the web camera and RGB-D camera, and matched to the
digitalized floor map so as to localize the user. The path and motion
guidance are generated afterwards to guide the user to a desired
destination. To improve the fitting between the rigid commands and
optimal machine decisions for human beings, we propose a context
based decision making mechanism on path planning that resolves
users’ confusions caused by incorrect observations. The software
modules are implemented in Robotics Operating System (ROS) and
the navigation system are tested with blindfolded sight persons. The
field experiments confirm the feasibility of the system prototype and
the proposed mechanism.
Keywords: Assistive Navigation; Context Based Decision
Making; Wearable System; Visually Impaired;
According to the factsheets of the World Health Organization
(WHO), 285 million people worldwide are blind or partially
sighted . People with normal vision orient them in physical
space and navigate from place to place with ease. However, it is
a challenging task for people who are blind or have significant
visual impairment to access unfamiliar environment even with
the help of electronic travel aids and vision techniques. Most
of the existing travel aids transform the visual and/or range
information to tactile display or audio guidance that informs the
user of nearby obstacles. These devices can be cane fitted handheld
or wearable devices to warn of obstacles ahead [2-6] or
provide ‘turn by turn’ guidance. The ability of visually impaired people to access, understand,
and explore unfamiliar environment will improve their inclusion
and integration into the society. It will also enhance employment
opportunities, foster independent living and produce economic
and social self-sufficiency .
After consulting with the blind individuals in organizations
such as the Lighthouse International NY, Computer Center for
Visually Impaired People (CCVIP) at CUNY Baruch College,
New York Institute for Special Education (NYISE), and the New
York State Commission for the Blind and Visually Handicapped
(CBVH), we realize that visually impaired people demand an
assistive technology that can provide them with safe and smooth
way-finding capabilities. Unfortunately, existing related assistive
technologies have various drawbacks and limitations.
SLAM is a process of building a map of unknown environment
while at the same time localizing the robot within the map. As an
extension of our previous works [17-20, 37] to further improve
the existing techniques in visually impaired user navigation,
we present the SLAM based navigation system using multiple
sensors which specifically fits the demand of visually impaired
user navigation in terms of reliability. Figure 1 shows the system
at work. It takes advantage of the SLAM technique to fuse the
inputs from multiple sensors and localize the user on the floor
plan, and represents the information and guidance in a high level
semantic map which contains necessary abstracted information
for human being.
Path planning for visually impaired is different with those
for robots. There are numerical optimal/suboptimal solutions
that handle uncertainty and incomplete information for robots.
However, as a human-centered system, the navigation system
shall seek to provide smooth and reliable user experience while
considering the facts that visually impaired users are prone to
be confused if conflicted or rigid commands are presented. Thus,
we propose a context based decision making mechanism in path
planning which fits the need of the navigation system. It takes
advantage of the fact that the goal is to reach the destination
Figure 1:The system at work (a) A team member wearing the system
(b) The RGBD sensor with slot which can stick onto the belt. An IMU is
mounted on the top left of the RGBD sensor (c) The camera on the head.
while it is not necessary to guarantee the perceptions are always
correct during the trip. The mechanism is flexible and capable of
being tailored to specific cases based on needs. This work is an
extension of our previous works [28-30, 37].
Contributions of this work are two folds. First, we developed
a wearable navigation system that guides the user navigating
from place to place. To the best of our knowledge, this is the first
system that performs SLAM and semantic path guiding on the
captured floor map. Second, we proposed a context-based path
planning mechanism that efficiently utilizes the user-oriented
data with uncertainty and specifically fits the need of visually
impaired navigation. The path planning mechanism takes
advantage of the property that motions can always be correct
even if the partial perceptions during the trip are wrong. To the
best of our knowledge, this is the first path planning mechanism
that provides smooth and reliable guidance while wrong
observation on landmarks may be presented in the context of
visually impaired navigation. The prototype is implemented and
This paper is organized as follows. related works; system
architecture and illustrates the details and algorithms in the
SLAM based navigation; context based decision making on path
planning; system implementation with experimental results and
ends with conclusion and the future works.
Starting from section III, the term “visually impaired
user” with “user” and “door number” with “room number” are
A number of works implemented inertial sensor to track
and localize the users [9-11]; however they lack accuracy and
reliability for blind user navigation. GPS/ GIS based approaches
fit to outdoor navigation demands but are powerless in indoor
Yang et al. proposed a localization system that uses
accelerometer embedded in a mobile phone to track a person
in a pre-collected RSS (received signal strength) map .
However, the RSS finger print based corrections requires
considerable pre-installations and pre-processing in creating the
RSS map. Moreover, it uses step counters and compasses for pose
estimation, which has accumulative errors over time. The same
problem happens to the localization and tracking system using
WiFi signal proposed by Kannan .
In recent years, there have been a few attempts to apply SLAM
technology in assistive navigation for visually impaired people.
The SWAN project  developed a software engine using the
Monte Carlo Localization and count on GPS for pedestrian outdoor
navigation. The research emphasis is placed on sonification to
guide the blind user; leaving many research issues untouched
such as how to detect environment features for updating believe.
The “Navatar project”  employs an IMU in the smartphone
and use a particle filter algorithm to help visually impaired
persons for indoor navigation. However, it assumes the indoor
map is known as a priori through manual annotation and uses
human as sensor to detect simple landmarks (i.e., intersections
of hall ways and open doors). This landmark confirmation by the
user is prone to false alarms since the multiple landmarks may
not be distinguishable by touch.
Lan et al., proposed an indoor localization system that tracks
a person using the accelerometer embedded in a waist mounted
mobile phone. It utilizes a floor-plan map to calibrate the
direction errors from the gyroscope by comparing the layout of
the building and the trajectory taken by the user . However,
the localization is based on dead reckoning and lacks landmark
A Co-robot Cane prototype is developed to aid the visually
impaired navigation. It uses a mounted 3-D camera to estimate
the pose, and a web-camera to recognize the major objects while
traveling. A reasonable possibility of success is proved in the
experiment. However, how to handle to failures is not mentioned.
Researchers have made significant progress in applying
advanced technologies in computer vision, robotics and artificial
intelligence to improve mobility and navigation of people with
special needs Y.H. Lee et al [20-22]. used a Kinect sensor to extract
orientation information of the blind users, by incorporating
visual odometry and feature based metric-topological SLAM
. An improved work utilizes the spatial feature and IMU to
guarantee the correctness of localization, under the condition
that the scenario has to be a traveled place .
Although a good number of navigation solutions for visually
impaired are presented, little of them provides a feasible solution
while incorrect observations are presented. The interactions are
not truly considered human-centric since the needs of visually
impaired are not taken good care. Be different with normal
sighted user, the visually impaired may not be able to provide that
feedback to correct error caused by sensor. On the other hand,
incorrect observation may frustrate the user while navigating,
especially when being guided walk back and forth. Fortunately,
in indoor navigation, the paths to the destination are very limited,
one or more wrong perceptions on landmarks do not instantly
lead to a different path. As long as later perceptions can correct
the overall understanding of the on environment so that the user
can be guided to the destination smoothly.
Our system uses a web camera and a 3-D camera to percept
the landmarks to localize the user on a digitalized floor plan. A
semantic map is abstracted to make the navigation feasible. A
context based path planner generates path and motion guidance
SLAM Based Localization
SLAM is a technology successfully used in robotic navigation,
which maintains a probabilistic representation of both the
subject’s pose and the locations of landmarks (i.e., the “belief”
of subject’s location and a “map” denoted with landmarks), and
refines recursively the pose representation and map in two
steps (i.e., motion and correction steps) [23-26]. In the motion
step, the robot pose is predicted using the robot motion model.
In the correction step, observations of landmarks are used to
refine the probabilistic pose representation on the map while
simultaneously updating the map with latest detected landmarks.
The SLAM principle is analogous to the navigation scenario
where a visually impaired user is required to find a conference
room in an office building. If a floor plan is accessible, i.e., captured
by head mounted camera or downloaded from the internet,
the system can digitalize it into a map before performing path
planning and navigation. The system can localize the walking
user by comparing the salient landmarks (e.g., door numbers)
on the map. The motion and observation steps continue until the
user reaches the target place.
Assistive navigations are challenging because the user not
only needs decent perception of the map of surroundings but also
demands suitable planned path to accomplish the navigation.
Figure 2 illustrates the system architecture. The hardware
includes wearable sensors including camera, IMU and RGBD
camera, interactive devices including speaker and bone
headphone and processing units including a laptop. The software
is composed of nine cooperative sub-modules. As follows, to
clarify their functionality, we introduce the software sub-modules
in the presence of a navigation scene.
As requested by the Fire Departments in most cities in US,
floor plans shall be posted at the entrances to all required exit
stairs, every elevator landing, and immediately inside all public
entrances to the public buildings. Thus, the user can use the
head mounted camera to scan and extract the floor plan before
digitalizing it into a grid/semantic map, by using the floor
plan digitalization module, right after he enters a building or
leaves an elevator. The user needs to let the system know the
destination room through the speech recognition module so as
to trigger the navigation. While moving, the user is localized by
the visual odometry module using RGB-D camera . The user
Figure 2:The system architecture.
can request the detection of room number by the door number
extraction module using camera when he touches the door. In
the meantime, the corners will be automatically detected by the
corner and wall detection module using depth images from
RGB-D camera. The door numbers and corners are regarded as
landmarks so as to match with the digitalized floor plan map as
well as update the particles through SLAM module. The IMU
is equipped to complement the orientation errors. A further
orientation revising is performed through the corner and
wall detection module. The path is planed through the path
planning module, where the context based decisions are made
and delivered to the user as motion commands and hints through
the text to speech module.
Visual odometry and local planar mapping
We apply our previous work -- fast visual odometry using
RGB-D camera  to provide raw pose of the user. It aligns
sparse features observed in the current RGB-D image on a model
of previous features. The model is persistent and is dynamically
updated from new observations using a Kalman filter. The
algorithm is capable of closing small-scale loops in indoor
environments online without any additional SLAM back-end
techniques; but like all other visual odometry approaches, it has
accumulative drifts over long time in the motion from place to
place. The drifts are revised using the IMU and further corrected
using the wall border extracted from depth images as described
At the same time, the depth images which are obtained by
the RGB-D sensor and presented in the form of 3D point clouds
are projected onto the planar coordinate and then stacked
together based on the corresponding poses estimated by the
visual odometry. Particularly, the system keeps only the depth
planar map with respect to the recent poses within certain travel
displacement as shown in Figure 3.
In order to make the user aware of their physical locations,
contextual information from visual landmarks such as floor plan
with signage, room number and corners are parameterized to the
Figure 3:Recent planar depth images are reorganized in a planar depth
local map according to user’s poses in visual odometry.
digitalized semantic map as in Figure 4.
Floor map digitalization: A heuristic method of extracting
layout information from a floor plan that employs room numbers
and corners, etc. to infer landmarks and way points are used
. A rule-based method is implemented to localize the position
of all room number labels as in Figure 5. Then the range of the
rooms and positions of their doors in the floor map are searched
by using a vertical and horizontal scan from the region of room
number. Assuming that all the rooms have their doors on the
hallway, anchor points are generated by using the room number
labels and corners.
Landmark extraction and matching: The visual landmarks
in the immediate vicinity of the user such as room numbers and
corners are extracted to localize the user.
A) An optical character recognition algorithm  is used
to localize the user when the user travels to the corresponding
physical locations. Specifically, the door number is extracted
after the user actively triggers the recognition through a verbal
command “door number detection” after he perceives the
existence of the door through the sense of touch. The benefits
are of two folds: one is to minimize the false alarm of un-wanted
misdetection; another is to greatly save the processing power
by image processing. If the detection fails to get any meaningful
result, it will prompt the user to trigger detection again.
B) The real-time depth images from the RGB-D camera are
used to detect corners. Specifically, the consequential depth
images from time to time are aligned using the raw poses obtained
by visual odometry. Thus, at a given time, the depth image to be
processed is the stacked depth image after overlapping recent
depth images according to their poses in the visual odometry. The
period is restricted by the measurement of physical displacement
calculated through visual odometry. Since the visual odometry
drift in a short period is not heavy, the resulting stacked depth
image is a good approximation to the shape of the surrounding
as shown in Figure 3.
Then the border is extracted after projecting the depth
image into horizontal plane. A revised Hough Transform 
after the Harris operator  is applied to obtain the corner
candidates from the image of extracted borders. The corner
candidates are confirmed as detected landmarks only if being
Figure 4:The semantic map is created based on a captured floor plan
image. The upper half shows an image containing the floor plan captured
near an elevator. The lower half shows the digitalized semantic
map and its semantic data in a form of adjacency matrix.
Figure 5:Room number extraction in floor plan digitalization
further confirmed by a shape filter of the stacked depth image.
The intuition is straight forward: since the stacked depth images
are always within an arbitrarily given displacement, it is easy to
perceive corners because the depth image is changing from long
and thin to short and thick (or long and thick) when approaching
After being confirmed, the corner candidates are matched
with the corner sets in the floor map to update the particles.
Particularly, the matching criterion is loosened, and thus a
corner perception will enlarge the weight of particles around a
few similar corners. This eliminates the possible failure caused
by false alarm and yet is sufficient in application.
C) At the same time, the scale between the metrics in the floor
plan and the real time scan can be obtained. As shown in Fig. 6,
the right hand side is the planar surrounding obtained from the
depth image of RGB-D while detecting a landmark and the left
hand side is the corresponding surrounding of the same landmark
on the floor plan. By comparing the width of the corridor next to
the corresponding landmark, the scale between the metrics in the
real time navigation and the floor plan can be easily calculated
and recursively updated.
Human machine interaction
Speech recognition module and text to speech module
are used to bridge the perceptions of the user and the system.
Details of the implementation using open source libraries [34,
35] are illustrated in Implementation and Experiments section.
Localization using particle filter
If the digitalized floor plan is treated as a map and the doors
and corners as landmarks, the localization of the user on the floor
plan with doors and corners labels is analogous to the localization
of a moving object on the map with preregistered landmarks.
Taking advantage of the integrated sensors, the prediction phase
adopts motion estimations from visual odometry module and
IMU, while the correction phase receives landmark confirmations
from door number extraction as well as corner and wall detection.
Particle filter: Particle filter is used to estimate the pose
distribution of the subject. x is used to denote the state, u the
motion input from visual odometry, s the measurement of door
number, z the measurement of corner and λ the measurement
of wall angle.
The localization state is represented through a set of particles
in the 2D planar space
Where (x, y) represents the position, θ is the orientation
angle of each particle and δ x(i) (·) is the impulse function
centered at the particle
x (i). The denser the particles in a state
space region, the higher the probability that the subject is in that
The distribution is represented through a set of weighted particles
The particles are drawn from a
by posterior distribution
while the weights
for each particle are computed according to the Importance
Sampling Principle (ISP):
By choosing the motion model
as the proposal
distribution, the weight update approximately becomes
The motion model in the prediction phase is based
on the output of visual odometry, which is composed of
and rotation model
the bivariate normal distribution and uniform distribution,
denote the translation and
rotation estimation from visual odometry, Σ and a denote the
corresponding covariance matrix and restriction parameter
obtained through sensor calibration, respectively. To project the
visual odometry estimations in its local frame on to the global
frame, a transformation after landmark matching is indispensable
and to be described later.
The perception model depends on the specific sensor and
task, and is assumed as a joint distribution of three independent
types of perceptions, e.g. corner, room number and wall angle,
Floor map and landmark matching:The localization is
meaningful only if it successfully localizes the user on the floor
In this work, we use room numbers and corners as the
landmarks to initialize and correct the translation and rotation
matrix, and further refine the rotation matrix using the depth
image collected by the RGBD camera.
We set up a global frame on the digitalized floor map which is
regarded as the ground truth. At each step, the visual odometry
algorithm  process the RGB-D data to estimate the pose of
the user and represent it in Visual Odometry (VO) frame whose
origin is located at the initial position when the system starts.
After the system detects two landmarks (e.g., doors or corridor
corners), the line segment in VO frame is used to represent the
actual heading of the user during last travel period. By matching
it with the corresponding line segment in the floor map, the
initial estimate of the user’s pose can be obtained. However, the
Figure 6:Scale between the two metrics of the visual odometry frame
and the floor plan frame is obtained by comparing h1 and h2.
Figure 7:Samples of consequential particle filter updates on the map
(a) The particles after initialization (b) The particles after the initial
perception update after detecting a room by its room number (c) The
particles after a few steps of motion updates without knowing the heading
orientation (d) The particles after another perception update of another
room (e) The particle updates after a few steps motion updates
after acknowledging the raw heading orientation.
visual odometry suffers from accumulative drifts especially in
featureless environment. The IMU reading is used to correct the
heading angle estimate while the user is walking. Then the particle
filter is applied to refine the estimation in two phases (motion/
prediction and correction/measure) iteratively. While the user
is moving, its pose is predicted by using visual odometry in VO
frame and then transformed to the global frame. The particles
are updated accordingly. Whenever a landmark is detected, the
corresponding perception model will be used in updating the
Note that the visual odometry poses are on the VO frame A
; the floor map and its visual landmarks are on the global frame
W ; the IMU’s poses are on the IMU frame B . As widely accepted
by the robotics society, L x is used to denote the pose in frame
L , composed by the planar position L õ and orientation Lθ
. Give two frames W and L , WLT is used to denote the 3 × 3
transformation matrix from L to W , composed by a translation
vectorWLT and a 2 × 2 rotation matrixWLR . Specifically, for a
given rotationα , the corresponding rotation matrix is
As shown in Algorithm 1, in the initial period the system
guides the user roaming around in order to discover and detect
landmarks. The user can actively trigger the room number
detection of the system by a verbal command after he touches a
door while the system passively detects corners.
In the “preliminary” stage, the orientation difference between
the VO frame and IMU frames is recorded as in line 3. While the
user is roaming, the pose is updated accordingly as line 5~6. If a
landmark is detected and no prior landmark is recorded, it simply
records this landmark’s positions in both the VO frame and the
global frame as line 8~10. When the next landmark is detected,
its corresponding positions in both frames can also be recorded.
Consequently, a correspondence can be obtained to calculate the
raw pose of the user as line 12~17. The ∠(õ) in line 15 denotes
the angle of vector õ ; R(α ) in line 16 denotes the rotation matrix
Figure 8:Samples of the wall angle estimation: The green dots indicate
the extracted border points; the red lines indicate the extracted line segments
of angleα . After that, the stage is updated from “preliminary” to
“normal”. Note that, the door numbers are unique but the walls
and corners are not. Thus, in the preliminary stage, only the door
numbers are accepted as landmarks. In line 21~24, the particles
are updated based on new detected landmarks.
There are two potential issues causing the orientation drifts.
First, the raw user pose obtained by matching the landmarks
on the VO frame and global frame is not accurate. Second,
the particle updates which revise the user pose may incur
accumulative errors. IMU can limit the accumulative drift but
cannot do anything with the initial estimation error. Recall that
a local planar map (Figure 3) is being updated while the user is
moving. It is easy to obtain the angle of wall in frame A by using
border extraction and linear regressions as shown in Fig. 8. At
the same time, it is feasible to find the surrounding wall’s angle
in the global frame W . Projecting the two angles onto the same
frame, it is straight forward to calculate the compensation for
orientation correction as line 25~ 27 in algorithms 1 (Figure 9).
This orientation revising is not frequently triggered. On one hand,
it needs to avoid the significant drift of visual odometry caused
by lacking visual features. On the other hand, the accumulative
orientation drift in a short period is limited since an IMU stays in
the loop. Line 29~31 denotes the motion model δ trans and δ rot
updates and the corresponding particles’ prediction.
Context Based Decision Making on Path
The observation result has a direct impact on the perception.
Furthermore, the determination of status updates is crucial to the
consequential decision making on motion. It is understandable
that oversimplification of the decision making might lead to an
inappropriate and even wrong decision. However, the effort on
the human centered decision making has been less paid. We
Figure 9:The intuition on orientation correction is that the wall border
from RGB-D data is obtainable, and the corresponding wall border on
floor map is given. Aligning them corrects orientation drift.
suggest that the decision making should be mostly treated as a
complicated problem, the scope of which should be expanded to
include the user character in the loop. Without an appropriate
approach to deal with the complexity of evaluation, it is likely to
come out with a best decision for robots but a terrible one for
Particularly in this work, it is realized that the room number
detection may result in incorrect results by a number of factors:
limitations on text extraction accuracy, interferes presented
within the view scope (Fig. 10), etc.
An incorrect detection may lead to a temporary loss in
localization, and might confuse the user if the system tries to
deliver the commands with regarding to the “shortest path” to
the user since it may leads to back and forth movements. For
visually impaired user navigation, a smooth user experience is on
the top of priorities since the user may not be able to provide as
much as feedback to correct the system as normal sighted people,
and is prone to feel less secure when receiving contradictory
commands. Fortunately, the special feature of indoor navigation
and the existing digitalized data enable a smooth and reliable
decision making on path and motion planning.
If cells in the adjacent matrix (corridor between anchors)
are regarded as the basic units, the navigation can be treated
as the traversal game from the starting cell to the destination.
It is obvious that the solution is limited. In other words, even if
the current perception is incorrect, the movement may still be
correct. Under the assumption that the incorrect observation
is the minority, the system is guaranteed to recover the correct
perception after multiple detection attempts on the trip.
as the probability of cellij
(i-th row and
j-th column) after k time’s independent detections where the
The confidence function C(Sij )
is formulated as
denotes the forward distance; a and b are empirical fraction factors which
control the preference on short distance and how likely to let the
user turn back. The forward distance is defined as the distance
of the shortest path that does not contain cellji from the j-th
anchor to the destination. In the case that no forward distance is
=0 . By comparing the
with a preset confidence threshold χ , the system is able to make
decisions on whether to let the user move along cellij.
An intuitive explanation on how the decision maker works is
as follows (see figure 4). Assume that the user walks on cell32 ,
and has some detections on landmarks. Due to some incorrect
detection, the user has a major belief on cell56 and minor believes
on cell32. Although the localization may be temporarily lost, the
system may prompt the user to move forward since it leads to
the anchor closer to the destination. After several detections, the
localization is back to be stable and the user’s previous motions
are identical to the desired motions. In another case, the belief on
cell52 and the believes on cell,23 are both major. Either keep moving
forward or turn backward depends on the factor b. Note that
when the confidence on localization is lower than a threshold, the
system will frequently warn the user to perform room number
detection in order to gain more observations on landmarks
As one of the major topics in visually impaired navigation,
the audio guidance principles should follow the principles of
scientific soundness, feasibility, and effectiveness so as to include
indispensable functionalities related to obstacle avoidance, user
preference and conciseness. The dedicated audio guidance is
studied in another work focusing on human sensing and visibility
Implementation and Experiments
In this section, we first illustrate the implementation in
experiment, and then discuss the results and reason. In order to
verify the system, a blind user participated in experiments but
the data in the following analyses is based on repeated blind fold
trials. The experiments were carried out on the ST-Hall sixth floor
in the Grove School of Engineering of CCNY as shown in Figure 4.
The hardware includes an ASUS Xtion PRO as the RGB-D
senor, a Phidgets Spatial 3/3/3 as the IMU and a Logitech C920
HD camera. We use a Samsung S3 laptop with speaker and
microphone as the human machine interaction base and a Lenovo
Y510 laptop as a data processing center. An additional laptop is
used since the bus bandwidth cannot handle all the inputs flow.
As described previously, the RGB-D camera is plugged onto the
user’s belt, the IMU is pasted on the RGB-D camera, the webcamera
is head mounted, and the laptops are backpacked.
The software is implemented on the platform of the robotics
operating system (ROS), the ccny-rgbd-tools are used as the
base of visual odometry , a wrapped character appearance
and structure modeling  is applied to extract room numbers.
The CMU pocketsphinx-speech-recognition  is taken as the
speech recognition tool, and the text to speech is achieved using
default package  in ROS.
Experiment and results
We first perform sub-system test on landmark detection and
localization, and finally perform the task oriented trail studies.
Room number detection: The first experiment is to exam
the successful rate of room number detection under different
First, the camera is mounted on the head of a blind folded
tester standing 60 centimeters away perpendicular to the room
number, and heading towards to it. The tester is not restricted to
keep steady during test. The detection is performed 20 times for
each of 10 different rooms. Second, the tester moves along the
Figure 10:Example of interferes on room number detection.
corridor, turns around and triggers the room number detection
20 times for each of 10 rooms so as to exam the successful rate
of room number detection in an almost realistic application. As a
part of the landmark detection, the system will notify the user if
the detection fails.
In Table 1, the successful rates are listed. The first time success
denotes that the user gets a correct output of room number after
a single trigger of detection. Multi-time success denotes that the
room number can be detected after the user triggers multiple
times of detection for the same door. The failure denotes a wrong
detection of a room number.
Obviously, the room number detection is reliable. One of the
reasons is that the outputs are restricted by the possible room
number data sets obtained from floor plan digitalization. The first
time success rate during the test is not very high while any motion
caused by user may interfere the image quality captured by the
camera thus leading to a less accurate detection. Additionally, the
room number does not always stay within the view of the camera.
However, after perceiving that the room number is not detected,
the user can trigger another round of detection, which enhances
the chance of obtaining the true room number. On the other
hand, the user may adjust his pose to present a better position.
The user may leave after being noticed that the room number is
detected and so the success rate plus failure rate does not equal
Localization drifts: To quantify the drift in the localization,
we designed an evaluation procedure as follows. An arbitrary
route is given on the corridor as indicated on Figure 11. The
subject starts the system localization and walks along the path.
The subject intentionally traverses all the landmarks on the path.
Table 1: Exam Table of Room Number Detection.
First time success
Figure 11:An arbitrary route is designed for the test. The blue segments
indicate the landmarks to be passively detected alone the path.
Finally, the drift in localization is calculated.
As shown in Figure 12, the localization drifts against the
ground truth are collected in five trials. It appears that the drifts
are within 0.2 meters in most of the time, which is accurate
enough for the navigation. Whenever a new landmark is detected
on the way, the drifts can be slightly reduced. The trial in black
does not converge in the figure, because the room number
detection reports a wrong landmark near the end. It takes a while
for the particles to converge.
Orientation drifts: A number of existing peer works suffer
from orientation drifts which potentially make the loop closure
more difficult. This work is inherently invulnerable to the
orientation drift because of the fact that at any time the real
time wall border obtained by RGB-D can be aligned with the wall
border on the digitalized floor map as shown on Figure 8 and
To show this, we setup evaluations under two different
conditions: one is with this orientation compensation, another is
not. The test process is the same as the last test for evaluating the
localization drift. The system runs two individual localizations in
the background with and without the orientation compensation.
The orientation drifts against the ground truth are collected, and
then averaged for both cases. Figure 13 shows that applying the
orientation compensation greatly remove the orientation drifts.
Navigation trials with context based decision maker:
The system is tested by blind folded users on the sixth floor of
the Engineering Building in CCNY. Three start-and-destination
pairs are chosen as the test cases shown in Figure 14 (refer to
Figure 4 for landmarks). The experiments are designed to test
the ability that the system is able to guide the user to accomplish
the tasks. Then, under the same experiment setup, a noise
generator is added to the room number detection module. It has
a 10% possibibility to generate a random room number on the
digitalized floor map whenever the room detection is performed.
It is shown in Table 2 that with the additional noise the
average navigation times are increased. There is little back and
forth motion even when wrong detection results are presented
early. But the system prompt the user to face to the doors and
perform door detection more frequently in order to enhance the
confidence on localization, especially when the initial localization
drift is heavy. One of the issues presented is that the feature
observation such as room number detection is quite slow. The
room number detection is performed only if commanded by the
user; and the user has to adjust his pose and stay steady before
In this paper, we present a wearable navigation system
Figure 12: The localization drifts.
Figure 13: The orientation drifts comparison.
Figure 14: The starting positions are marked in red and destinations
are marked in blue. For example, the starting position and destination
are noted as “1start” and “1dest.”, respectively.
Table 2: Average Complete Time (s).
with context based decision making for visually impaired. By
integrating multiple sensors including RGB-D camera, IMU,
and web camera, the localization and trajectory of the user
are functionally achieved using particle filter. We also have
presented a unique approach to correct the estimated user
orientation by aligning RGB-D wall border with floor map wall
border, which significantly improves the fusion performance
in indoor localization. The system is able to deliver semantic
information to the user and help him to reach a destination. A
context based decision helps to gain correct motion decision even
if the temporary localization is scattered. It greatly enhances the
user experience and especially fits the need of visually impaired.
The future works are of two folds: one is to replace the hardware
by mobile devices, and the other is to utilize more stable feature
such as sparse features which minimize the need for major
feature such as room number.
The authors would like to thank Dr. Chieko Asakawa and Dr.
Hironobu Takagi of IBM Accessibility Research for providing
guidelines on our application. We acknowledge Dr. Ivan
Dryanovski for his works on visual odometry. Prof. Jizhong Xiao
would like to thank the Alexander von Humboldt Foundation for
providing the Humboldt Research Fellowship for Experienced
Researchers to support the research on assistive navigation in
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